The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC a...The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.展开更多
In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) t...In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neuralnetwork based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradientinformation partial deriv y/partial deriv u for optimizing the parameters of controller. Comparedwith many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzycontroller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM online. Lastly, in order to evaluate the performance of the proposed control system, the presentedcontrol system is applied to continuously stirre'd tank reactor (CSTR). Simulation comparisons,based on control effect and output error, with general fuzzy controller and feed-forward neuralfuzzy network controller (FNFNC), are conducted. In addition, the rates of convergence of RNNMrespectively using RPE algorithm and gradient learning algorithm are also compared. The results showthat the proposed control system is better for controlling uncertain and nonlinear processes.展开更多
文摘The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.
基金The author is now working as a research fellow in the Department of Chemical & Biomolecular Engineering,Faculty of Engineering,National University of Singapore,Singapore,119260.
文摘In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neuralnetwork based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradientinformation partial deriv y/partial deriv u for optimizing the parameters of controller. Comparedwith many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzycontroller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM online. Lastly, in order to evaluate the performance of the proposed control system, the presentedcontrol system is applied to continuously stirre'd tank reactor (CSTR). Simulation comparisons,based on control effect and output error, with general fuzzy controller and feed-forward neuralfuzzy network controller (FNFNC), are conducted. In addition, the rates of convergence of RNNMrespectively using RPE algorithm and gradient learning algorithm are also compared. The results showthat the proposed control system is better for controlling uncertain and nonlinear processes.
